





'''simple docstring'''


import gc
import threading
import time

import psutil
import torch

class  lowerCAmelCase_		:




 def __init__(						self		)     ->				Union[str, Any]:
     _lowerCAmelCase						      =   psutil.Process()
     _lowerCAmelCase						      =   False




 def 				_snake_case (						self		)     ->				Any:
     _lowerCAmelCase						      =   -1

     while True:
         _lowerCAmelCase						      =   max(self.process.memory_info().rss      ,  self.cpu_memory_peak		)

         # can't sleep or will not catch the peak right (this comment is here on purpose)
         if not self.peak_monitoring:
             break




 def 				_snake_case (						self		)     ->				Dict:
     _lowerCAmelCase						      =   True
     _lowerCAmelCase						      =   threading.Thread(target=self.peak_monitor		)
     _lowerCAmelCase						      =   True
     self.thread.start()





 def 				_snake_case (						self		)     ->				Optional[Any]:
     _lowerCAmelCase						      =   False
     self.thread.join()
     return self.cpu_memory_peak







_SCREAMING_SNAKE_CASE									=				PeakCPUMemory()


def 						__a():



    '''simple docstring'''





    _lowerCAmelCase						      =   {"time": time.time()}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    _lowerCAmelCase						      =   psutil.Process().memory_info().rss
    cpu_peak_tracker.start()

    # GPU mem
    for i in range(torch.cuda.device_count()     ):
        _lowerCAmelCase						      =   torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     )
    torch.cuda.reset_peak_memory_stats()

    return measures


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   {"time": time.time() - start_measures["time"]}

    gc.collect()
    torch.cuda.empty_cache()

    # CPU mem
    _lowerCAmelCase						      =   (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
    _lowerCAmelCase						      =   (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

    # GPU mem
    for i in range(torch.cuda.device_count()     ):
        _lowerCAmelCase						      =   (torch.cuda.memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20
        _lowerCAmelCase						      =   (torch.cuda.max_memory_allocated(SCREAMING_SNAKE_CASE_     ) - start_measures[str(SCREAMING_SNAKE_CASE_     )]) / 2**20

    return measures





def 						__a(SCREAMING_SNAKE_CASE_	:       Any		,							SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    print(F'''{description}:'''     )
    print(F'''- Time: {measures['time']:.2f}s'''     )
    for i in range(torch.cuda.device_count()     ):
        print(F'''- GPU {i} allocated: {measures[str(SCREAMING_SNAKE_CASE_     )]:.2f}MiB'''     )
        _lowerCAmelCase						      =   measures[F'''{i}-peak''']
        print(F'''- GPU {i} peak: {peak:.2f}MiB'''     )
    print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB'''     )
    print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB'''     )







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






